Polychlorinated naphthalenes and other dioxin-like compounds in Elbe River sediments
Bibliographic record
Abstract
Contamination of Elbe River (Germany) sediments with dioxin-like toxicants was investigated following the 500-year flood (flood that statistically occurs once in 500 years) of 2002. It was hypothesized that large amounts of particulate matter from river beds and associated dioxin-like toxicants were mobilized and transported during this flood event. The investigation focused on polychlorinated naphthalenes (PCNs) that have not been determined previously in the Elbe River. The in vitro H4IIE-luc assay was used as an overall measure for toxicants capable of binding to the aryl hydrocarbon receptor (AhR). The assay was combined with congener-specific instrumental analyses and fractionation to quantify PCN contributions to total AhR-mediated activity relative to polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) and polychlorinated biphenyls (PCBs). Penta- to octachloronaphthalene concentrations of 30 ng/kg dry weight up to 13 microg/kg dry weight were found in Elbe River sediments downstream of Bitterfeld. Concentrations of penta- to octachloronaphthalenes, however, were only approximately 3 microg/kg dry weight at a site in the vicinity of Bitterfeld, where a level of approximately 3 mg/kg dry weight was reported before the flood. Also, the congener pattern of PCNs at this site changed after the flood, and PCN patterns reported previously for Bitterfeld and assigned to chlor-alkali electrolysis with graphite electrodes could now be observed at the sites from downstream of Bitterfeld and Magdeburg. Whereas PCDD/Fs dominated the dioxin-like activity in the middle and lower Elbe River, PCNs contributed as much as 10% of the total AhR-mediated activity. The contribution of PCBs was less significant (maximum, 0.2%). Thus, in Elbe River sediments, PCNs should be considered as relevant contaminants and be included in future monitoring and risk assessment programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".